fangwei00-jin/DeepSatModels

Deep learning models for remote sensing applications

Repository for training land cover recognition models for satellite imagery

Featured Papers

The following papers are featured in this repository:

• ViTs for SITS: Vision Transformers for Satellite Image Time Series (CVPR 2023). More information in README_TSVIT.md.
• Context-self contrastive pretraining for crop type semantic segmentation (IEEE Transactions on Geoscience and Remote Sensing). More information in README_CSCL.md.

Setting up a python environment

• Follow the instruction in https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html for downloading and installing Miniconda

• Open a terminal in the code directory

• Create an environment using the .yml file:

    conda env create -f deepsatmodels_env.yml
  

• Activate the environment:

    source activate deepsatmodels   
  

• Install required version of torch:

    conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch-nightly
  

Initial steps for setting up experiments

• Specify the base directory and paths for the training and evaluation files in the "data/datasets.yaml" file.
• Utilize a distinct ".yaml" configuration file for each experiment. Example files can be found in the "configs" folder. These files contain default values corresponding to parameters used in the associated studies.
• Adjust the ".yaml" configuration files as needed to train with your custom data.
• Refer to the instructions provided in the specific README.MD files for additional guidance on setting up and running your experiments.